Data-Driven Dynamic Pricing and Inventory Control with Censored Demand and Limited Price Changes

2015 ◽  
Author(s):  
Boxiao Chen ◽  
Xiuli Chao
Keyword(s):  
Inventory Control ◽  
Dynamic Pricing ◽  
Data Driven ◽  
Price Changes

Technical Note—Data-Based Dynamic Pricing and Inventory Control with Censored Demand and Limited Price Changes

2020 ◽  
Vol 68 (5) ◽  
pp. 1445-1456 ◽  
Author(s):  
Boxiao Chen ◽  
Xiuli Chao ◽  
Yining Wang
Keyword(s):  
Adverse Effect ◽  
Dynamic Pricing ◽  
Convergence Rates ◽  
Technical Note ◽  
Data Driven ◽  
Selling Price ◽  
Price Changes ◽  
Demand Distribution ◽  
Demand Structure ◽  
Price Relationship

Pricing and inventory replenishment are important operations decisions for firms such as retailers. To make these decisions effectively, a firm needs to know the demand distribution and its dependency on selling price, which is usually estimated using sales data at various testing price levels. Although more testing prices can lead to a better estimation of the demand–price relationship, frequent price changes are costly and come with adverse effect such as customers’ negative perception. In this article, data-driven algorithms are developed that learn the demand structure with constraints on the number of price changes. These algorithms are shown to converge to the optimal clairvoyant solution, and the convergence rates are the best possible in terms of profit loss.

scholarly journals Sampling-Based Approximation Schemes for Capacitated Stochastic Inventory Control Models

2019 ◽  
Vol 44 (2) ◽  
pp. 668-692 ◽  
Author(s):  
Wang Chi Cheung ◽  
David Simchi-Levi
Keyword(s):  
Inventory Control ◽  
Sample Average Approximation ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Data Driven ◽  
Approximation Schemes ◽  
Stochastic Inventory ◽  
Stochastic Inventory Control ◽  
Sample Average ◽  
Full Knowledge

We study the classical multiperiod capacitated stochastic inventory control problems in a data-driven setting. Instead of assuming full knowledge of the demand distributions, we assume that the demand distributions can only be accessed through drawing random samples. Such data-driven models are ubiquitous in practice, where the cumulative distribution functions of the underlying random demand are either unavailable or too complex to work with. We consider the sample average approximation (SAA) method for the problem and establish an upper bound on the number of samples needed for the SAA method to achieve a near-optimal expected cost, under any level of required accuracy and prespecified confidence probability. The sample bound is polynomial in the number of time periods as well as the confidence and accuracy parameters. Moreover, the bound is independent of the underlying demand distributions. However, the SAA requires solving the SAA problem, which is #P-hard. Thus, motivated by the SAA analysis, we propose a polynomial time approximation scheme that also uses polynomially many samples. Finally, we establish a lower bound on the number of samples required to solve this data-driven newsvendor problem to near-optimality.

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